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Han Chinese, Korean and Japanese are the main populations of East Asia, and Han Chinese presents a gradient admixture from north to south. There are differences among the East Asian populations in genetic structure. To achieve fine-scale genetic classification of southern (S-) and northern (N-) Han Chinese, Korean and Japanese individuals in this study, we collected and analyzed 1185 ancestry informative SNPs (AISNPs) from previous literature reports and our laboratory findings. First, two machine learning algorithms, softmax and randomForest, were used to build genetic classification models. Then, phylogenetic tree, STRUCTURE and principal component analysis were used to evaluate the performance of classification for different AISNP panels. The 234-AISNP panel achieved a fine-scale differentiation among the target populations in four classification schemes. The accuracy of the softmax model was 92%, which realized the accurate classification of the S-Han, N-Han, Korean and Japanese individuals. The two machine learning models tested in this study provided important references for the high-resolution discrimination of close-range populations and will be useful tools to optimize marker panels for developing forensic DNA ancestry inference systems.

Many ancestry informative SNP (AISNP) panels have been published. Ancestry resolution in them varies from three to eight continental clusters of populations depending on the panel used. However, none of these panels differentiates well among East Asian populations. To meet this need, we have developed a 74 AISNP panel after analyzing a much larger number of SNPs for Fst and allele frequency differences between two geographically close population groups within East Asia. The 74 AISNP panel can now distinguish at least 10 biogeographic groups of populations globally: Sub-Saharan Africa, North Africa, Europe, Southwest Asia, South Asia, North Asia, East Asia, Southeast Asia, Pacific and Americas. Compared with our previous 55-AISNP panel, Southeast Asia and North Asia are two newly assignable clusters. For individual ancestry assignment, the likelihood ratio and ancestry components were analyzed on a different set of 500 test individuals from 11 populations. All individuals from five of the test populations - Yoruba (YRI), European (CEU), Han Chinese in Henan (CHNH), Rondonian Surui (SUR) and Ticuna (TIC) - were assigned to their appropriate geographical regions unambiguously. For the other test populations, most of the individuals were assigned to their self-identified geographical regions with a certain degree of overlap with adjacent populations. These alternative ancestry components for each individual thus help give a clearer picture of the possible group origins of the individual. We have demonstrated that the new AISNP panel can achieve a deeper resolution of global ancestry., (Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.)

Aim: To genotype and evaluate a panel of single-nucleotide polymorphisms for individual identification (IISNPs) in three Chinese populations: Chinese Han, Uyghur, and Tibetan., Methods: Two previously identified panels of IISNPs, 86 unlinked IISNPs and SNPforID 52-plex markers, were pooled and analyzed. Four SNPs were included in both panels. In total, 132 SNPs were typed on Sequenom MassARRAY® platform in 330 individuals from Han Chinese, Uyghur, and Tibetan populations. Population genetic indices and forensic parameters were determined for all studied markers., Results: No significant deviation from Hardy-Weinberg equilibrium was observed for any of the SNPs in 3 populations. Expected heterozygosity (He) ranged from 0.144 to 0.500 in Han Chinese, from 0.197 to 0.500 in Uyghur, and from 0.018 to 0.500 in Tibetan population. Wright's Fst values ranged from 0.0001 to 0.1613. Pairwise linkage disequilibrium (LD) calculations for all 132 SNPs showed no significant LD across the populations (r(2)<0.147). A subset of 58 unlinked IISNPs (r(2)<0.094) with He>0.450 and Fst values from 0.0002 to 0.0536 gave match probabilities of 10-25 and a cumulative probability of exclusion of 0.999992., Conclusion: The 58 unlinked IISNPs with high heterozygosity have low allele frequency variation among 3 Chinese populations, which makes them excellent candidates for the development of multiplex assays for individual identification and paternity testing.

Background: Monoamine oxidases (MAOs) catalyze the metabolism of dopaminergic neurotransmitters. Polymorphisms of isoforms MAOA and MAOB have been implicated in the etiology of mental disorders such as schizophrenia. Association studies detected these polymorphisms in several populations, however the data have not been conclusive to date. Here, we investigated the association of MAOA and MAOB polymorphisms with schizophrenia in a Han Chinese population., Methods: Two functional single nucleotide polymorphisms (SNPs), rs6323 of MAOA and rs1799836 of MAOB, were selected for association analysis in 537 unrelated schizophrenia patients and 536 healthy controls. Single-locus and Haplotype associations were calculated., Results: No differences were found in the allelic distribution of rs6323. The G allele of rs1799836 was identified as a risk factor in the development of schizophrenia (P = 0.00001). The risk haplotype rs6323T-rs1799836G was associated with schizophrenia in female patients (P = 0.0002), but the frequency difference was not significant among male groups., Conclusions: Our results suggest that MAOB is a susceptibility gene for schizophrenia. In contrast, no significant associations were observed for the MAOA functional polymorphism with schizophrenia in Han Chinese. These data support further investigation of the role of MAO genes in schizophrenia., (© 2011 Wei et al; licensee BioMed Central Ltd.)